Xiaojun Li , Shuiji Zhang

Biotechnology Research Center, Cuixi Academy of Biotechnology, Zhuji, 311800, China
Author    Correspondence author
Computational Molecular Biology, 2024, Vol. 14, No. 1   doi: 10.5376/cmb.2024.14.0001
Received: 14 Nov., 2023    Accepted: 24 Dec., 2023    Published: 04 Jan., 2024

This is an open access article published under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Li X.J., and Zhang S.J., 2024, Genomic prediction and its association with the development of dementia disease in the elderly, Computational Molecular Biology, 14(1): 1-8 (doi: 10.5376/cmb.2024.14.0001)

Dementia is a severe neurological disorder involving complex interactions between various genetic and environmental factors. This paper explores the association between genomic prediction and the development of dementia in the elderly. Through a systematic review of existing research, the study delves into genomics, the genetic basis of dementia, and the etiology related to the genome. The research further examines the methods and applications of genomic prediction, focusing on the use of polygenic risk scores and machine learning algorithms in dementia studies. Through case analyses of large-scale genomic studies, key genes associated with dementia, such as Alzheimer's disease, are revealed. Additionally, the paper thoroughly analyzes the major findings of existing research, emphasizing the filling of knowledge gaps and the provision of new insights. Finally, the paper discusses the challenges faced by genomic prediction, including methodological difficulties, challenges in data interpretation, ethical and privacy concerns, and more. Looking ahead to future research directions, the paper highlights the establishment of personalized genomic prediction models, the application of new technologies, and the potential value of genomic prediction in early diagnosis and prevention of dementia.

Elderly dementia disease; Genomic prediction; Genetics; Polygenic risk scores; Machine learning algorithms